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Advanced particle swarm assisted genetic algorithm for constrained optimization problems

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  • Manoj Dhadwal
  • Sung Jung
  • Chang Kim

Abstract

A novel hybrid evolutionary algorithm is developed based on the particle swarm optimization (PSO) and genetic algorithms (GAs). The PSO phase involves the enhancement of worst solutions by using the global-local best inertia weight and acceleration coefficients to increase the efficiency. In the genetic algorithm phase, a new rank-based multi-parent crossover is used by modifying the crossover and mutation operators which favors both the local and global exploration simultaneously. In addition, the Euclidean distance-based niching is implemented in the replacement phase of the GA to maintain the population diversity. To avoid the local optimum solutions, the stagnation check is performed and the solution is randomized when needed. The constraints are handled using an effective feasible population based approach. The parameters are self-adaptive requiring no tuning based on the type of problems. Numerical simulations are performed first to evaluate the current algorithm for a set of 24 benchmark constrained nonlinear optimization problems. The results demonstrate reasonable correlation and high quality optimum solutions with significantly less function evaluations against other state-of-the-art heuristic-based optimization algorithms. The algorithm is also applied to various nonlinear engineering optimization problems and shown to be excellent in searching for the global optimal solutions. Copyright Springer Science+Business Media New York 2014

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  • Manoj Dhadwal & Sung Jung & Chang Kim, 2014. "Advanced particle swarm assisted genetic algorithm for constrained optimization problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 781-806, July.
    • Handle: RePEc:spr:coopap:v:58:y:2014:i:3:p:781-806
    DOI: 10.1007/s10589-014-9637-0
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    References listed on IDEAS

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    1. Kalyanmoy Deb & Soumil Srivastava, 2012. "A genetic algorithm based augmented Lagrangian method for constrained optimization," Computational Optimization and Applications, Springer, vol. 53(3), pages 869-902, December.
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    Cited by:

    1. Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    2. Gao, Shujun & de Silva, Clarence W., 2018. "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 323-345.

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